{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-05-21T01:26:56.626382Z","iopub.execute_input":"2023-05-21T01:26:56.627006Z","iopub.status.idle":"2023-05-21T01:27:00.537833Z","shell.execute_reply.started":"2023-05-21T01:26:56.626949Z","shell.execute_reply":"2023-05-21T01:27:00.535936Z"},"trusted":true},"execution_count":18,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[18], line 12\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Input data files are available in the read-only \"../input/\" directory\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dirname, _, filenames \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39mwalk(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/kaggle/input\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m filenames:\n\u001b[1;32m     14\u001b[0m         \u001b[38;5;28mprint\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(dirname, filename))\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:377\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    374\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 377\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[43mentry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_dir\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m    379\u001b[0m     \u001b[38;5;66;03m# If is_dir() raises an OSError, consider that the entry is not\u001b[39;00m\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;66;03m# a directory, same behaviour than os.path.isdir().\u001b[39;00m\n\u001b[1;32m    381\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"code","source":"import torch\nimport torchvision\nimport torchvision.transforms as transforms\n\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\nbatch_size = 32\ntrain_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TRAIN\", transform=transform)\ntest_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TEST\", transform=transform)\ntrain_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\ntest_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:43:05.268651Z","iopub.execute_input":"2023-05-21T02:43:05.269043Z","iopub.status.idle":"2023-05-21T02:43:05.931117Z","shell.execute_reply.started":"2023-05-21T02:43:05.269011Z","shell.execute_reply":"2023-05-21T02:43:05.930190Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np\n\n# functions to show an image\n\n\ndef imshow(img):\n    img = img / 2 + 0.5     # unnormalize\n    npimg = img.numpy()\n    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n    plt.show()\n\n\n# get some random training images\ndataiter = iter(train_loader)\n# images, labels = dataiter.next()\nimages, labels = next(dataiter)\n\n# show images\nimshow(torchvision.utils.make_grid(images))\n# print labels\nprint(labels.shape)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:43:07.512079Z","iopub.execute_input":"2023-05-21T02:43:07.512420Z","iopub.status.idle":"2023-05-21T02:43:07.987929Z","shell.execute_reply.started":"2023-05-21T02:43:07.512393Z","shell.execute_reply":"2023-05-21T02:43:07.986855Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"name":"stdout","text":"torch.Size([32])\n","output_type":"stream"}]},{"cell_type":"code","source":"import math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\n\ndef conv3x3(in_planes, out_planes, stride=1):\n    \"3x3 convolution with padding\"\n    return nn.Conv2d(\n        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n    )\n\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n    ):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(inplanes, planes, stride)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = conv3x3(planes, planes)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.downsample = downsample\n        self.stride = stride\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n      \nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n    ):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(\n            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\nclass Res2NetBottleneck(nn.Module):\n\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, scales=4\n    ):\n        super(Res2NetBottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.ModuleList([nn.Conv2d(planes//scales, planes//scales, kernel_size=3, padding=1, bias=False) for _ in range(scales-1)])\n        self.bn2 = nn.ModuleList([nn.BatchNorm2d(planes // scales) for _ in range(scales-1)])\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n        self.scales = scales\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n        \n#         print(out.shape)\n#         print(\"Begin Forward\")\n        xs = torch.chunk(out, self.scales, 1)\n        ys = []\n        \n        for s in range(self.scales):\n            if s == 0:\n                ys.append(xs[s])\n            elif s == 1:\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s]))))\n            else:\n#                 print(xs[s].shape, ys[-1].shape)\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s] + ys[-1]))))\n        out = torch.cat(ys, 1)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out    \n\nclass ResNet(nn.Module):\n    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n        self.inplanes = 64\n        super(ResNet, self).__init__()\n        self.network_type = network_type\n        # different model config between ImageNet and CIFAR\n        if network_type == \"ImageNet\":\n            self.conv1 = nn.Conv2d(\n                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n            )\n            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n            self.avgpool = nn.AvgPool2d(7)\n        else:\n            self.conv1 = nn.Conv2d(\n                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n            )\n\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n\n        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type)\n        self.layer2 = self._make_layer(\n            block, 128, layers[1], stride=2, att_type=att_type\n        )\n        self.layer3 = self._make_layer(\n            block, 256, layers[2], stride=2, att_type=att_type\n        )\n        self.layer4 = self._make_layer(\n            block, 512, layers[3], stride=2, att_type=att_type\n        )\n\n        self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n        init.kaiming_normal_(self.fc.weight)\n        for key in self.state_dict():\n            if key.split(\".\")[-1] == \"weight\":\n                if \"conv\" in key:\n                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n                if \"bn\" in key:\n                    if \"SpatialGate\" in key:\n                        self.state_dict()[key][...] = 0\n                    else:\n                        self.state_dict()[key][...] = 1\n            elif key.split(\".\")[-1] == \"bias\":\n                self.state_dict()[key][...] = 0\n\n    def _make_layer(self, block, planes, blocks, stride=1, att_type=None):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(\n                    self.inplanes,\n                    planes * block.expansion,\n                    kernel_size=1,\n                    stride=stride,\n                    bias=False,\n                ),\n                nn.BatchNorm2d(planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(\n            block(\n                self.inplanes,\n                planes,\n                stride,\n                downsample,\n                use_triplet_attention=att_type == \"TripletAttention\",\n            )\n        )\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(\n                block(\n                    self.inplanes,\n                    planes,\n                    use_triplet_attention=att_type == \"TripletAttention\",\n                )\n            )\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        if self.network_type == \"ImageNet\":\n            x = self.maxpool(x)\n\n        x = self.layer1(x)\n#         print(\"Begin layer2\")\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        if self.network_type == \"ImageNet\":\n            x = self.avgpool(x)\n        else:\n            x = F.avg_pool2d(x, 4)\n        x = x.view(x.size(0), -1)\n        x = self.fc(x)\n        return x\n\n\ndef ResidualNet(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(BasicBlock, [2, 2, 2, 2], network_type, num_classes, att_type)\n#         model = ResNet(Bottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(BasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n        # model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(Bottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\n  \ndef Res2Net(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(Res2NetBottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(Res2NetBottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(Res2NetBottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(Res2NetBottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\n\ndevice=\"cuda\"\nres2net=Res2Net(\"CIFAR100\",18,100,None).to(device)\nprint(res2net)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:43:09.024007Z","iopub.execute_input":"2023-05-21T02:43:09.024828Z","iopub.status.idle":"2023-05-21T02:43:09.387585Z","shell.execute_reply.started":"2023-05-21T02:43:09.024779Z","shell.execute_reply":"2023-05-21T02:43:09.386645Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"resnet_bottleneck = ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(resnet_bottleneck)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:16:45.003248Z","iopub.execute_input":"2023-05-21T02:16:45.003595Z","iopub.status.idle":"2023-05-21T02:16:45.404928Z","shell.execute_reply.started":"2023-05-21T02:16:45.003563Z","shell.execute_reply":"2023-05-21T02:16:45.404000Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"resnet_basicblock = ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(resnet_basicblock)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:43:13.742246Z","iopub.execute_input":"2023-05-21T02:43:13.742599Z","iopub.status.idle":"2023-05-21T02:43:13.974772Z","shell.execute_reply.started":"2023-05-21T02:43:13.742572Z","shell.execute_reply":"2023-05-21T02:43:13.973855Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (fc): Linear(in_features=512, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"import torch.optim as optim\n\ncriterion = nn.CrossEntropyLoss()\n# 这里需要修改优化器\noptimizer = torch.optim.Adam(resnet_basicblock.parameters(), lr=0.005)\nprint(len(train_loader))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:43:20.814732Z","iopub.execute_input":"2023-05-21T02:43:20.815111Z","iopub.status.idle":"2023-05-21T02:43:20.823805Z","shell.execute_reply.started":"2023-05-21T02:43:20.815074Z","shell.execute_reply":"2023-05-21T02:43:20.822876Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"1250\n","output_type":"stream"}]},{"cell_type":"code","source":"from tqdm import tqdm\ndef train(net, epoch):\n    net.train()\n    # Loop over each batch from the training set\n    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n    for batch_idx, (data, target) in enumerate(train_tqdm):\n        # Copy data to GPU if needed\n        data = data.to(device)\n        target = target.to(device)\n        # Zero gradient buffers\n        optimizer.zero_grad()\n        # Pass data through the network\n        output = net(data)\n        # Calculate loss\n        loss = criterion(output, target)\n        # Backpropagate\n        loss.backward()\n        # Update weights\n        optimizer.step()  # w - alpha * dL / dw\n        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n    return net\n\ndef validate(net, lossv,top1AccuracyList,top5AccuracyList):\n    net.eval()\n    val_loss = 0\n    top1Correct = 0\n    top5Correct = 0\n    for index,(data, target) in enumerate(test_loader):\n        data = data.to(device)\n        labels = target.to(device)\n        outputs = net(data)\n        val_loss += criterion(outputs, labels).data.item()\n        _, top1Predicted = torch.max(outputs.data, 1)\n        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n        top1Correct += (top1Predicted == labels).cpu().sum().item()\n        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n\n    val_loss /= len(test_loader)\n    lossv.append(val_loss)\n\n    top1Acc=100*top1Correct / len(test_loader.dataset)\n    top5Acc=100*top5Correct / len(test_loader.dataset)\n    top1AccuracyList.append(top1Acc)\n    top5AccuracyList.append(top5Acc)\n    \n    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n#     accv.append(accuracy)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:43:25.949128Z","iopub.execute_input":"2023-05-21T02:43:25.949477Z","iopub.status.idle":"2023-05-21T02:43:25.963300Z","shell.execute_reply.started":"2023-05-21T02:43:25.949448Z","shell.execute_reply":"2023-05-21T02:43:25.962432Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"import time\n\nres2netLossv = []\nres2netTop1AccuracyList = []   # top1准确率列表\nres2netTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    res2net = train(res2net, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(res2net, res2netLossv,res2netTop1AccuracyList,res2netTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))\n","metadata":{"execution":{"iopub.status.busy":"2023-05-21T01:49:13.011430Z","iopub.execute_input":"2023-05-21T01:49:13.011841Z","iopub.status.idle":"2023-05-21T02:06:15.828626Z","shell.execute_reply.started":"2023-05-21T01:49:13.011805Z","shell.execute_reply":"2023-05-21T02:06:15.827292Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [03:14<00:00,  6.43it/s, loss=2.34]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3122, Top1Accuracy: 4021/10000 (40%) Top5Accuracy: 7104.0/10000 (71%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.63]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2263, Top1Accuracy: 4265/10000 (43%) Top5Accuracy: 7338.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.77] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1583, Top1Accuracy: 4519/10000 (45%) Top5Accuracy: 7467.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.59] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1745, Top1Accuracy: 4616/10000 (46%) Top5Accuracy: 7521.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.23] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2429, Top1Accuracy: 4666/10000 (47%) Top5Accuracy: 7601.0/10000 (76%)\n\nFinished Training\nTraining complete in 16m 11s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\nresnet_bottleneckLossv = []\nresnet_bottleneckTop1AccuracyList = []   # top1准确率列表\nresnet_bottleneckTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    resnet_bottleneck = train(resnet_bottleneck, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(resnet_bottleneck, resnet_bottleneckLossv,resnet_bottleneckTop1AccuracyList,resnet_bottleneckTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:28:46.022872Z","iopub.execute_input":"2023-05-21T02:28:46.023917Z","iopub.status.idle":"2023-05-21T02:35:44.185983Z","shell.execute_reply.started":"2023-05-21T02:28:46.023866Z","shell.execute_reply":"2023-05-21T02:35:44.184783Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [01:16<00:00, 16.41it/s, loss=3.08]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 4.4866, Top1Accuracy: 3599/10000 (36%) Top5Accuracy: 6654.0/10000 (67%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [01:15<00:00, 16.48it/s, loss=2.47]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.7050, Top1Accuracy: 3805/10000 (38%) Top5Accuracy: 6840.0/10000 (68%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [01:15<00:00, 16.48it/s, loss=2.54]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3556, Top1Accuracy: 4170/10000 (42%) Top5Accuracy: 7197.0/10000 (72%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [01:16<00:00, 16.37it/s, loss=2.09] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2923, Top1Accuracy: 4282/10000 (43%) Top5Accuracy: 7332.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [01:15<00:00, 16.56it/s, loss=1.12] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3284, Top1Accuracy: 4342/10000 (43%) Top5Accuracy: 7364.0/10000 (74%)\n\nFinished Training\nTraining complete in 6m 20s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\nresnet_basicblockLossv = []\nresnet_basicblockTop1AccuracyList = []   # top1准确率列表\nresnet_basicblockTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    resnet_basicblock = train(resnet_basicblock, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(resnet_basicblock, resnet_basicblockLossv,resnet_basicblockTop1AccuracyList,resnet_basicblockTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:47:46.788808Z","iopub.execute_input":"2023-05-21T02:47:46.789213Z","iopub.status.idle":"2023-05-21T02:51:58.960508Z","shell.execute_reply.started":"2023-05-21T02:47:46.789178Z","shell.execute_reply":"2023-05-21T02:51:58.959190Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [00:43<00:00, 29.07it/s, loss=1.95] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2133, Top1Accuracy: 4380/10000 (44%) Top5Accuracy: 7424.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [00:43<00:00, 28.84it/s, loss=1.24] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.4084, Top1Accuracy: 4323/10000 (43%) Top5Accuracy: 7323.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [00:42<00:00, 29.22it/s, loss=1.14] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.6218, Top1Accuracy: 4388/10000 (44%) Top5Accuracy: 7352.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [00:43<00:00, 28.68it/s, loss=0.478]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.0349, Top1Accuracy: 4320/10000 (43%) Top5Accuracy: 7195.0/10000 (72%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [00:43<00:00, 28.54it/s, loss=0.142] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.0746, Top1Accuracy: 4373/10000 (44%) Top5Accuracy: 7311.0/10000 (73%)\n\nFinished Training\nTraining complete in 3m 37s\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}